Decision-Making

MIT SMR’s winter 2025 issue offers advice for leaders on making better decisions by seeking out divergent perspectives.

Anchors based on past projects can improve scheduling accuracy and on-time project completion.

A collaborative model of influence emphasizes making the best decision for the organization rather than winning a debate.

Consider these three takeaways for leaders on how to avoid pitfalls while using generative AI tools in decision-making.

Be on the lookout for these practices that prevent boards of directors from functioning effectively.

Research shows that better decision-making can be achieved through the strategic use of artificial intelligence systems.

Leaders can use these tactics to make meetings more engaging and move people from apathy to energy.

Board committees are key to strong corporate governance. Learn five ways to maximize the effectiveness of their members.

Survey report: What 1,000 business and data leaders think about using generative AI for analytics.

Persuading employees to use data to make better decisions requires both top-down leadership and grassroots initiatives.

Leaders who explore, evaluate, and refine their values set themselves up to make better decisions.

These tips can help leaders develop their own and employees’ ability to apply ethical judgment in difficult situations.

Leaders’ delegation decisions should reflect the trust they have in both their people and organizational processes.

This issue of MIT SMR focuses on sustainability, customer and employee engagement, and strategic planning.

A data monetization matrix can help leaders assess opportunities and approaches for converting data into revenue.

Simple rules of thumb can result in more effective decision-making than comprehensive analysis.

Black hat workshops can help you model your company’s competitive landscape before making the next strategic move.

Companies benefit when employees across demographics have an equal opportunity to affect organizational decision-making.

Agility can lead to negative outcomes if leaders don’t recognize the pitfalls in its processes.

UC Berkeley’s Ziad Obermeyer discusses how machine learning and AI are being used for medical research and diagnoses.